SMART
Lead Research Organisation:
University of Liverpool
Department Name: Mech, Materials & Aerospace Engineering
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
George-Williams H
(2018)
Extending the survival signature paradigm to complex systems with non-repairable dependent failures
in Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Liang T
(2016)
Risk-informed analysis of the large break loss of coolant accident and PCT margin evaluation with the RISMC methodology
in Nuclear Engineering and Design
Oparaji U
(2017)
Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems
in Neural Networks
Patelli E.
(2018)
An efficient computational strategy for robust maintenance scheduling: Application to corroded pipelines
in Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018
Tolo S
(2019)
Robust on-line diagnosis tool for the early accident detection in nuclear power plants
in Reliability Engineering & System Safety
Tolo S
(2018)
An open toolbox for the reduction, inference computation and sensitivity analysis of Credal Networks
in Advances in Engineering Software
| Description | We have developed efficient and reliable tools for on-line monitoring able to deal with uncertainty. Not only we can predict the size of the LOCA but also have confidence associated with the prediction. |
| Exploitation Route | The methodology developed is general and can be adopted to predict with confidence any signal of interest in different sectors. The methodology has been also integrated on the open source software OpenCossan |
| Sectors | Aerospace Defence and Marine Chemicals Creative Economy Digital/Communication/Information Technologies (including Software) Energy Manufacturing including Industrial Biotechology Transport |
| URL | http://www.riskinstitute.uk |
| Description | The research has allowed the development of robust methodologies for the online monitoring of safety-critical systems. Although the methodologies were applied to a nuclear system, they are applicable to other systems where the identification of the current system state is important (e.g. for system health monitoring, conditional maintenance, mitigation and recovery actions). Of one the main challenges were to provide the operator (decision maker) and the analyst with some quantifiable confidence in the methodology. To address this challenge, we have developed robust neural networks supported by Bayesian statistics. The developed tool not only provides a reliable estimation of the state of the system but also provides the associated confidence bounds with the prediction. In addition, the computational tools developed during the project allow to deal with noise data, and conflicting information coming from sensors. The methodology has been further improved by Jonathan Sadeghi in his PhD thesis. The developed methods have been released as open-source software that is freely available on the git repository of OpenCossan software (www.cossan.co.uk). The approach has been also used in the feasibility project (PROMAP) supported by NNL for predicting the properties of nuclear material using AI. |
| First Year Of Impact | 2017 |
| Sector | Digital/Communication/Information Technologies (including Software),Energy |
| Description | Enhanced Methodologies for Advanced Nuclear System Safety (eMEANSS) |
| Amount | £854,922 (GBP) |
| Funding ID | EP/T016329/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2022 |
| End | 11/2025 |
| Description | FIS360 Innovation Consultant |
| Amount | £25,000 (GBP) |
| Funding ID | NNL/GC_551 |
| Organisation | National Physical Laboratory |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 12/2021 |
| End | 03/2022 |
| Description | Impact Acceleration Account |
| Amount | £15,000 (GBP) |
| Organisation | University of Liverpool |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 02/2019 |
| End | 07/2019 |
| Title | Smartool |
| Description | Open source matlab toolbox for on-line monitoring and robust prediction |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Impact | Publication on Reliability Engineering & System Safety Volume 186, June 2019, Pages 110-119 https://doi.org/10.1016/j.ress.2019.02.015 |
| URL | https://cossan.co.uk/svn/SmartTool |
| Description | Case study for SMART project |
| Organisation | Bhabbha Atomic Research Centre |
| Country | India |
| Sector | Public |
| PI Contribution | Based on the simulated accident data provided by BARC, a robust online diagnostic system for the nuclear reactor. The aim of the analysis is to identify the severity of the Loss Of Coolant Accident (LOCA) events on the basis of selected instrument signals: for this purpose, the main task of the implemented Artificial Neural Network is to recognize the pattern drawn by the input signals in time and to provide, on the basis of this information, the severity of the break in output. This is quantified in terms of break size, expressed in comparison with the double-ended rupture of the largest pipe in the reactor coolant system. For instance, a 200% break indicates the free discharge of the primary coolant from both the broken ends of the main pipe (this is generally considered the worst accident that can occur in a water circuit). |
| Collaborator Contribution | The project partner simulated accident scenario of the primary heat transport system of a 220MWe pressurised heavy water reactor, whose design has a double containment with a vapour suppression pool. The main aim of the containment is to limit the release of radioactivity under normal and accident conditions, both at the ground level and through the stack. The accident scenario is a Loss Of Coolant Accident (LOCA) involving a double ended guillotine rupture of the reactor inlet header. In case of such accident, the vapour suppression pool is designed to limit the peak pressure and temperature in the containment, allowing the complete condensation of the incoming steam and limiting the leakage of fission products to the surrounding environment. In addition to this, several strategies (e.g. dissolving, trapping, entraining mechanisms) are in place to perform the removal of the fission products that reach the pool. |
| Impact | Development of open source toolbox for on-line monitoring of critical systems |
| Start Year | 2017 |
| Description | Development of online diagnostic system for nuclear power plants |
| Organisation | University of Portsmouth |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Development of an opensource toolbox for robust online monitoring system |
| Collaborator Contribution | Training of Artificial Neural Networks with different architectures |
| Impact | Development of an opensource toolbox for robust online monitoring system |
| Start Year | 2017 |
| Title | SMARTool |
| Description | All the methods developed in this research project have been implemented in to a open source MATLAB toolbox named SMARTool. The SMARTool contains procudures and scripts used to indentify and training ANN architectures adopting the Machine Learning Toolbox for MATLAB, while the pruning of the one hidden layer architecture has been performed adopting the NNSYSID toolbox. The NNSYSID is a toolbox for the identification of non-linear dynamic systems with artificial neural networks, often used as a benchmark, which implements several algorithms for ANN training and pruning. The SMARTool toolbox provides the adaptive Bayesian model averaging method, dataset organization and re-population methods (i.e. for linear and cubic spline interpolation as well as for Gaussian mixture sampling) and dedicated uncertainty quantification techniques, such as the error estimation for series association. In addition, the SMARTool can also access the advanced uncertainty quantification techniques provided by OpenCossan. |
| Type Of Technology | Software |
| Year Produced | 2018 |
| Open Source License? | Yes |
| Impact | The open source toolbox is freely available and it can easily be integrated with other software |
| Title | Toolboox for OpenCossan |
| Description | OpenCOSSAN is a tool for uncertainty quantification and management. It represents the core of COSSAN software under continuous development at the Institute for Risk and Uncertainty,University of Liverpool, UK. All the algorithms and methods have been coded in a Matlab toolbox allowing numerical analysis, reliability analysis, simulation, sensitivity, optimization, robust design. OpenCossan is coded exploiting the object-oriented Matlab programming environment, where it is possible to define specialized solution sequences, which include reliability methods, sensitivity analysis, optimization strategies, surrogate models and parallel computing strategies. The computational framework is organized in packages. A package is a namespace for organizing classes and interfaces in a logical manner, which makes large software project OpenCossan easier to manage. A class describes a set of objects with common characteristics such as data structures and methods. Objects, that are instances of classes can be aggregated forming more complex objects and proving solutions for practical problem in a compact, organized and manageable format. The structure of the software allows for extensive modularity and efficient code re-utilization. Objects (instances of a class) can be aggregated forming more complex objects with methods providing solutions for practical problem in a compact, organized and manageable format. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | Bayesian Belief Networks, more commonly known as Bayesian Networks, are a probabilistic graphical model based on the use of directed acyclic graphs, integrating graph theory with the robustness of Bayesian statistics. The graphical framework of such models consists of nodes, representing the variables of the problem of interest, connected to each other by edges, generally arrows, that depict the dependency link existing between two nodes. The main aim of the Bayesian Network approach is to factorize the probability of a complex event exploiting the knowledge regarding the dependencies existing among its sub-parts. In order to overcome the limitations associated with traditional Bayesian Networks, the integration of such approach with the imprecise probability theory has attracted increasing attention in the scientific community leading to the formulation and study of Credal Networks. Further efforts and research are strongly required in order to enhance the attractivness of Credal Networks outside the academic world and to ensure the reliability and efficiency of their performance in real-world applications. These aims represent the core of the Credal Networks toolbox developed within the OpenCossan framework: well known and novel methodologies are integrated in the software in order to provide the implementation, manipulation and analysis of Credal Networks. |
| URL | http://www.cossan.co.uk/software/open-cossan-engine.php |